OpenRaiser/Intern-Atlas huggingface dataset:
Present and Future of AI in Renewable Energy Domain : A Comprehensive Survey
Venue: arXiv cs.LG 2024
Year: 2024

Artificial intelligence (AI) has become a crucial instrument for streamlining processes in various industries, including electrical power systems, as a result of recent digitalization. Algorithms for artificial intelligence are data-driven models that are based on statistical learning theory and are used as a tool to take use of the data that the power system and its users generate. Initially, we perform a thorough literature analysis of artificial intelligence (AI) applications related to renewable energy (RE). Next, we present a thorough analysis of renewable energy factories and assess their suitability, along with a list of the most widely used and appropriate AI algorithms. Nine AI-based strategies are identified here to assist Renewable Energy (RE) in contemporary power systems. This survey paper comprises an extensive review of the several AI techniques used for renewable energy as well as a methodical analysis of the literature for the study of various intelligent system application domains across different disciplines of renewable energy. This literature review identifies the performance and outcomes of nine different research methods by assessing them, and it aims to distill valuable insights into their strengths and limitations. This study also addressed three main topics: using AI technology for renewable power generation, utilizing AI for renewable energy forecasting, and optimizing energy systems. Additionally, it explored AI's superiority over conventional models in controllability, data handling, cyberattack prevention, smart grid implementation, robotics- AI's significance in shaping the future of the energy industry. Furthermore, this article outlines future directions in the integration of AI for renewable energy.
A note on MDS Property of Circulant Matrices
Venue: arXiv cs.CR 2024
Year: 2024

In $2014$, Gupta and Ray proved that the circulant involutory matrices over the finite field $\mathbb{F}_{2^m}$ can not be maximum distance separable (MDS). This non-existence also extends to circulant orthogonal matrices of order $2^d \times 2^d$ over finite fields of characteristic $2$. These findings inspired many authors to generalize the circulant property for constructing lightweight MDS matrices with practical applications in mind. Recently, in $2022,$ Chatterjee and Laha initiated a study of circulant matrices by considering semi-involutory and semi-orthogonal properties. Expanding on their work, this article delves into circulant matrices possessing these characteristics over the finite field $\mathbb{F}_{2^m}.$ Notably, we establish a correlation between the trace of associated diagonal matrices and the MDS property of the matrix. We prove that this correlation holds true for even order semi-orthogonal matrices and semi-involutory matrices of all orders. Additionally, we provide examples that for circulant, semi-orthogonal matrices of odd orders over a finite field with characteristic $2$, the trace of associated diagonal matrices may possess non-zero values.
Identifying Easy Instances to Improve Efficiency of ML Pipelines for Algorithm-Selection
Venue: arXiv cs.LG 2024
Year: 2024

Algorithm-selection (AS) methods are essential in order to obtain the best performance from a portfolio of solvers over large sets of instances. However, many AS methods rely on an analysis phase, e.g. where features are computed by sampling solutions and used as input in a machine-learning model. For AS to be efficient, it is therefore important that this analysis phase is not computationally expensive. We propose a method for identifying easy instances which can be solved quickly using a generalist solver without any need for algorithm-selection. This saves computational budget associated with feature-computation which can then be used elsewhere in an AS pipeline, e.g., enabling additional function evaluations on hard problems. Experiments on the BBOB dataset in two settings (batch and streaming) show that identifying easy instances results in substantial savings in function evaluations. Re-allocating the saved budget to hard problems provides gains in performance compared to both the virtual best solver (VBS) computed with the original budget, the single best solver (SBS) and a trained algorithm-selector.
Deep Learning for Prediction and Classifying the Dynamical behaviour of Piecewise Smooth Maps
Venue: arXiv cs.LG 2024
Year: 2024

This paper explores the prediction of the dynamics of piecewise smooth maps using various deep learning models. We have shown various novel ways of predicting the dynamics of piecewise smooth maps using deep learning models. Moreover, we have used machine learning models such as Decision Tree Classifier, Logistic Regression, K-Nearest Neighbor, Random Forest, and Support Vector Machine for predicting the border collision bifurcation in the 1D normal form map and the 1D tent map. Further, we classified the regular and chaotic behaviour of the 1D tent map and the 2D Lozi map using deep learning models like Convolutional Neural Network (CNN), ResNet50, and ConvLSTM via cobweb diagram and phase portraits. We also classified the chaotic and hyperchaotic behaviour of the 3D piecewise smooth map using deep learning models such as the Feed Forward Neural Network (FNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN). Finally, deep learning models such as Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) are used for reconstructing the two parametric charts of 2D border collision bifurcation normal form map.
Meta-learning and Data Augmentation for Stress Testing Forecasting Models
Venue: arXiv cs.LG 2024
Year: 2024

The effectiveness of univariate forecasting models is often hampered by conditions that cause them stress. A model is considered to be under stress if it shows a negative behaviour, such as higher-than-usual errors or increased uncertainty. Understanding the factors that cause stress to forecasting models is important to improve their reliability, transparency, and utility. This paper addresses this problem by contributing with a novel framework called MAST (Meta-learning and data Augmentation for Stress Testing). The proposed approach aims to model and characterize stress in univariate time series forecasting models, focusing on conditions where they exhibit large errors. In particular, MAST is a meta-learning approach that predicts the probability that a given model will perform poorly on a given time series based on a set of statistical time series features. MAST also encompasses a novel data augmentation technique based on oversampling to improve the metadata concerning stress. We conducted experiments using three benchmark datasets that contain a total of 49.794 time series to validate the performance of MAST. The results suggest that the proposed approach is able to identify conditions that lead to large errors. The method and experiments are publicly available in a repository.
Collective Bit Flipping-Based Decoding of Quantum LDPC Codes
Venue: arXiv cs.IT 2024
Year: 2024

Quantum low-density parity-check (QLDPC) codes have been proven to achieve higher minimum distances at higher code rates than surface codes. However, this family of codes imposes stringent latency requirements and poor performance under iterative decoding, especially when the variable degree is low. In this work, we improve both the error correction performance and decoding latency of variable degree-3 (dv-3) QLDPC codes under iterative decoding. Firstly, we perform a detailed analysis of the structure of a well-known family of QLDPC codes, i.e., hypergraph product-based codes. Then, we propose a decoding approach that stems from the knowledge of harmful configurations apparent in these codes. Our decoding scheme is based on applying a modified version of bit flipping (BF) decoding, namely two-bit bit flipping (TBF) decoding, which adds more degrees of freedom to BF decoding. The granularity offered by TBF decoding helps us design sets of decoders that operate in parallel and can collectively decode error patterns appearing in harmful configurations of the code, thus addressing both the latency and performance requirements. Finally, simulation results demonstrate that the proposed decoding scheme surpasses other iterative decoding approaches for various dv-3 QLDPC codes.
Reducing the Memory Footprint of 3D Gaussian Splatting
Venue: arXiv cs.CV 2024
Year: 2024

3D Gaussian splatting provides excellent visual quality for novel view synthesis, with fast training and real-time rendering; unfortunately, the memory requirements of this method for storing and transmission are unreasonably high. We first analyze the reasons for this, identifying three main areas where storage can be reduced: the number of 3D Gaussian primitives used to represent a scene, the number of coefficients for the spherical harmonics used to represent directional radiance, and the precision required to store Gaussian primitive attributes. We present a solution to each of these issues. First, we propose an efficient, resolution-aware primitive pruning approach, reducing the primitive count by half. Second, we introduce an adaptive adjustment method to choose the number of coefficients used to represent directional radiance for each Gaussian primitive, and finally a codebook-based quantization method, together with a half-float representation for further memory reduction. Taken together, these three components result in a 27 reduction in overall size on disk on the standard datasets we tested, along with a 1.7 speedup in rendering speed. We demonstrate our method on standard datasets and show how our solution results in significantly reduced download times when using the method on a mobile device.
Boosting Bitcoin Minute Trend Prediction Using the Separation Index
Venue: arXiv cs.CE 2024
Year: 2024

Predicting the trend of Bitcoin, a highly volatile cryptocurrency, remains a challenging task. Accurate forecasting holds immense potential for investors and market participants dealing with High Frequency Trading systems. The purpose of this study is to demonstrate the significance of using a systematic approach toward selecting informative observations for enhancing Bitcoin minute trend prediction. While a multitude of data collection methods exist, a crucial barrier remains: efficiently selecting the most informative data for building powerful prediction models. This study tackles this challenge head-on by introducing the Separation Index, a groundbreaking tool for fast and effective data (feature) subset selection. The Separation Index operates by measuring the improvement in class separability (i.e. upward vs. downward trends) with each added feature set. This innovative metric guides the creation of a highly informative dataset, maximizing the model's ability to differentiate between price movements. Our research demonstrates the effectiveness of this approach, achieving unprecedented accuracy in minute-scale Bitcoin trend prediction, surpassing the performance of previous studies. This significant advancement paves the way for a new era of data-driven decision-making in the dynamic world of cryptocurrency markets.
Sound Field Synthesis with Acoustic Waves
Venue: arXiv cs.SD 2024
Year: 2024

We propose a practical framework to synthesize the broadband sound-field on a small rigid surface based on the physics of sound propagation. The sound-field is generated as a composite map of two components: the room component and the device component, with acoustic plane waves as the core tool for the generation. This decoupling of room and device components significantly reduces the problem complexity and provides accurate rendering of the sound-field. We describe in detail the theoretical foundations, and efficient procedures of the implementation. The effectiveness of the proposed framework is established through rigorous validation under different environment setups.
Stable Tool-Use with Flexible Musculoskeletal Hands by Learning the Predictive Model of Sensor State Transition
Venue: arXiv cs.RO 2024
Year: 2024

The flexible under-actuated musculoskeletal hand is superior in its adaptability and impact resistance. On the other hand, since the relationship between sensors and actuators cannot be uniquely determined, almost all its controls are based on feedforward controls. When grasping and using a tool, the contact state of the hand gradually changes due to the inertia of the tool or impact of action, and the initial contact state is hardly kept. In this study, we propose a system that trains the predictive network of sensor state transition using the actual robot sensor information, and keeps the initial contact state by a feedback control using the network. We conduct experiments of hammer hitting, vacuuming, and brooming, and verify the effectiveness of this study.
MixTex: Unambiguous Recognition Should Not Rely Solely on Real Data
Venue: arXiv cs.CV 2024
Year: 2024

This paper introduces MixTex, an end-to-end LaTeX OCR model designed for low-bias multilingual recognition, along with its novel data collection method. In applying Transformer architectures to LaTeX text recognition, we identified specific bias issues, such as the frequent misinterpretation of $e-t$ as $e^{-t}$. We attribute this bias to the characteristics of the arXiv dataset commonly used for training. To mitigate this bias, we propose an innovative data augmentation method. This approach introduces controlled noise into the recognition targets by blending genuine text with pseudo-text and incorporating a small proportion of disruptive characters. We further suggest that this method has broader applicability to various disambiguation recognition tasks, including the accurate identification of erroneous notes in musical performances. MixTex's architecture leverages the Swin Transformer as its encoder and RoBERTa as its decoder. Our experimental results demonstrate that this approach significantly reduces bias in recognition tasks. Notably, when processing clear and unambiguous images, the model adheres strictly to the image rather than over-relying on contextual cues for token prediction.
Toward Ubiquitous 3D Object Digitization: A Wearable Computing Framework for Non-Invasive Physical Property Acquisition
Venue: arXiv cs.GR 2024
Year: 2024

Accurately digitizing physical objects is central to many applications, including virtual/augmented reality, industrial design, and e-commerce. Prior research has demonstrated efficient and faithful reconstruction of objects' geometric shapes and visual appearances, which suffice for digitally representing rigid objects. In comparison, physical properties, such as elasticity and pressure, are also indispensable to the behavioral fidelity of digitized deformable objects. However, existing approaches to acquiring these quantities either rely on invasive specimen collection or expensive/bulky laboratory setups, making them inapplicable to consumer-level usage. To fill this gap, we propose a wearable and non-invasive computing framework that allows users to conveniently estimate the material elasticity and internal pressure of deformable objects through finger touches. This is achieved by modeling their local surfaces as pressurized elastic shells and analytically deriving the two physical properties from finger-induced wrinkling patterns. Together with photogrammetry-reconstructed geometry and textures, the two estimated physical properties enable us to faithfully replicate the motion and deformation behaviors of several deformable objects. For the pressure estimation, our model achieves a relative error of 3.5%. In the interaction experiments, the virtual-physical deformation discrepancy measures less than 10.1%. Generalization to objects of irregular shape further demonstrates the potential of our approach in practical applications. We envision this work to provide insights for and motivate research toward democratizing the ubiquitous and pervasive digitization of our physical surroundings in daily, industrial, and scientific scenarios.
Unlocking Continual Learning Abilities in Language Models
Venue: arXiv cs.LG 2024
Year: 2024

Language models (LMs) exhibit impressive performance and generalization capabilities. However, LMs struggle with the persistent challenge of catastrophic forgetting, which undermines their long-term sustainability in continual learning (CL). Existing approaches usually address the issue by incorporating old task data or task-wise inductive bias into LMs. However, old data and accurate task information are often unavailable or costly to collect, hindering the availability of current CL approaches for LMs. To address this limitation, we introduce $\textbf{MIGU}$ ($\textbf{M}$agn$\textbf{I}$tude-based $\textbf{G}$radient $\textbf{U}$pdating for continual learning), a rehearsal-free and task-label-free method that only updates the model parameters with large magnitudes of output in LMs' linear layers. MIGU is based on our observation that the L1-normalized magnitude distribution of the output in LMs' linear layers is different when the LM models deal with different task data. By imposing this simple constraint on the gradient update process, we can leverage the inherent behaviors of LMs, thereby unlocking their innate CL abilities. Our experiments demonstrate that MIGU is universally applicable to all three LM architectures (T5, RoBERTa, and Llama2), delivering state-of-the-art or on-par performance across continual finetuning and continual pre-training settings on four CL benchmarks. For example, MIGU brings a 15.2% average accuracy improvement over conventional parameter-efficient finetuning baselines in a 15-task CL benchmark. MIGU can also seamlessly integrate with all three existing CL types to further enhance performance. Code is available at https://github.com/wenyudu/MIGU.
A Comprehensive Solution to Connect Speech Encoder and Large Language Model for ASR
Venue: arXiv cs.LG 2024
Year: 2024

Recent works have shown promising results in connecting speech encoders to large language models (LLMs) for speech recognition. However, several limitations persist, including limited fine-tuning options, a lack of mechanisms to enforce speech-text alignment, and high insertion errors especially in domain mismatch conditions. This paper presents a comprehensive solution to address these issues. We begin by investigating more thoughtful fine-tuning schemes. Next, we propose a matching loss to enhance alignment between modalities. Finally, we explore training and inference methods to mitigate high insertion errors. Experimental results on the Librispeech corpus demonstrate that partially fine-tuning the encoder and LLM using parameter-efficient methods, such as LoRA, is the most cost-effective approach. Additionally, the matching loss improves modality alignment, enhancing performance. The proposed training and inference methods significantly reduce insertion errors.
SetBERT: Enhancing Retrieval Performance for Boolean Logic and Set Operation Queries
Venue: arXiv cs.CL 2024
Year: 2024

We introduce SetBERT, a fine-tuned BERT-based model designed to enhance query embeddings for set operations and Boolean logic queries, such as Intersection (AND), Difference (NOT), and Union (OR). SetBERT significantly improves retrieval performance for logic-structured queries, an area where both traditional and neural retrieval methods typically underperform. We propose an innovative use of inversed-contrastive loss, focusing on identifying the negative sentence, and fine-tuning BERT with a dataset generated via prompt GPT. Furthermore, we demonstrate that, unlike other BERT-based models, fine-tuning with triplet loss actually degrades performance for this specific task. Our experiments reveal that SetBERT-base not only significantly outperforms BERT-base (up to a 63% improvement in Recall) but also achieves performance comparable to the much larger BERT-large model, despite being only one-third the size.
Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models
Venue: arXiv cs.CL 2024
Year: 2024

Large language models (LLMs) have demonstrated impressive reasoning capabilities, particularly in textual mathematical problem-solving. However, existing open-source image instruction fine-tuning datasets, containing limited question-answer pairs per image, do not fully exploit visual information to enhance the multimodal mathematical reasoning capabilities of Multimodal LLMs (MLLMs). To bridge this gap, we address the lack of high-quality, diverse multimodal mathematical datasets by collecting 40K high-quality images with question-answer pairs from 24 existing datasets and synthesizing 320K new pairs, creating the MathV360K dataset, which enhances both the breadth and depth of multimodal mathematical questions. We introduce Math-LLaVA, a LLaVA-1.5-based model fine-tuned with MathV360K. This novel approach significantly improves the multimodal mathematical reasoning capabilities of LLaVA-1.5, achieving a 19-point increase and comparable performance to GPT-4V on MathVista's minitest split, and yielding leading performance on Math-V and MathVerse. Furthermore, Math-LLaVA demonstrates enhanced generalizability, showing substantial improvements on the MMMU benchmark. Our research highlights the importance of dataset diversity and synthesis in advancing MLLMs' mathematical reasoning abilities. The code and data are available at: \url{https://github.com/HZQ950419/Math-LLaVA}.
BlockLLM: Memory-Efficient Adaptation of LLMs by Selecting and Optimizing the Right Coordinate Blocks
Venue: arXiv cs.LG 2024
Year: 2024

Training large language models (LLMs) for pretraining or adapting to new tasks and domains has become increasingly critical as their applications expand. However, as the model and the data sizes grow, the training process presents significant memory challenges, often requiring a prohibitive amount of GPU memory that may not be readily available. Existing methods such as low-rank adaptation (LoRA) add trainable low-rank matrix factorizations, altering the training dynamics and limiting the model's parameter search to a low-rank subspace. GaLore, a more recent method, employs Gradient Low-Rank Projection to reduce the memory footprint, in the full parameter training setting. However GaLore can only be applied to a subset of the LLM layers that satisfy the "reversibility" property, thus limiting their applicability. In response to these challenges, we introduce BlockLLM, an approach inspired by block coordinate descent. Our method carefully selects and updates a very small subset of the trainable parameters without altering any part of its architecture and training procedure. BlockLLM achieves state-of-the-art performance in both finetuning and pretraining tasks, while reducing the memory footprint of the underlying optimization process. Our experiments demonstrate that fine-tuning with only less than 5% of the parameters, BlockLLM achieves state-of-the-art perplexity scores on the GLUE benchmarks. On Llama model pretrained on C4 dataset, BlockLLM is able to train with significantly less memory than the state-of-the-art, while still maintaining competitive performance.